Guozheng Li

Embedding visual representations within original hierarchical tables can mitigate additional cognitive load stemming from the division of users’ attention. The created hierarchical table visualizations can help users understand and explore complex data with multi-level attributes. However, because of many options available for transforming hierarchical tables and selecting subsets for embedding, the design space of hierarchical table visualizations becomes vast, and the construction process turns out to be tedious, hindering users from constructing hierarchical table visualizations with many data insights efficiently. We propose InsigHTable, a mixed-initiative and insight-driven hierarchical table transformation and visualization system. We first define data insights within hierarchical tables, which consider the hierarchical structure in the table headers. Since hierarchical table visualization construction is a sequential decision-making process, InsigHTable integrates a deep reinforcement learning framework incorporating an auxiliary rewards mechanism. This mechanism addresses the challenge of sparse rewards in constructing hierarchical table visualizations. Within the deep reinforcement learning framework, the agent continuously optimizes its decision-making process to create hierarchical table visualizations to uncover more insights by collaborating with analysts. We demonstrate the usability and effectiveness of InsigHTable through two case studies and sets of experiments. The results validate the effectiveness of the deep reinforcement learning framework and show that InsigHTable can facilitate users to construct hierarchical table visualizations and understand underlying data insights.

Media bias refers to bias in news reporting and coverage that exists pervasively. By identifying media bias, social scientists can understand the different perspectives held by media outlets in news reporting. Existing studies only focus on the analysis of media bias of isolated incidents, but neglect their sustained characteristics. Thus, they cannot provide a comprehensive understanding of specific news topics. We develop BiaSeer, a visual analytics system for identifying and understanding sustained bias of media outlets. BiaSeer employs an overview-to-detail approach for interactive identification of media bias. The overview assists users in determining the analysis scope of media outlets. In addition, it visualizes the variances in coverage patterns between selected media outlets using a matrix visualization to facilitate the identification of biased news articles. BiaSeer visualizes the sustained bias in the context of the events evolution. It first summarizes news articles into events based on a keyword co-occurrence graph and then connects events into a narrative structure using a path-aware story tree construction method. In addition, BiaSeer integrates a sustained bias computation algorithm and enables analysts to compare the narrative structures of different media outlets using the juxtaposition-based visualization approach. We conducted a user experiment to validate the effectiveness of BiaSeer in helping social scientists understand news topics and the usability of visualization designs. To examine the effectiveness of BiaSeer, we conducted a case study with social scientists on the topics of the Russia-Ukraine conflict. The results demonstrate the utility and usability of BiaSeer in efficiently analyzing media bias and attaining a well-rounded understanding of news topics.

When using exploratory visual analysis to examine multivariate hierarchical data, users often need to query data to narrow down the scope of analysis. However, formulating effective query expressions remains a challenge for multivariate hierarchical data, particularly when datasets become very large. To address this issue, we develop a declarative grammar, HiRegEx (Hierarchical data Regular Expression), for querying and exploring multivariate hierarchical data. Rooted in the extended multi-level task topology framework for tree visualizations (e-MLTT), HiRegEx delineates three query targets (node, path, and subtree) and two aspects for querying these targets (features and positions), and uses operators developed based on classical regular expressions for query construction. Based on the HiRegEx grammar, we develop an exploratory framework for querying and exploring multivariate hierarchical data and integrate it into the TreeQueryER prototype system. The exploratory framework includes three major components: top-down pattern specification, bottom-up data-driven inquiry, and context-creation data overview. We validate the expressiveness of HiRegEx with the tasks from the e-MLTT framework and showcase the utility and effectiveness of TreeQueryER system through a case study involving expert users in the analysis of a citation tree dataset.

Tabular visualization techniques integrate visual representations with tabular data to avoid additional cognitive load caused by splitting users’ attention. However, most of the existing studies focus on simple flat tables instead of hierarchical tables, whose complex structure limits the expressiveness of visualization results and affects users’ efficiency in visualization construction. We present HiTailor, a technique for presenting and exploring hierarchical tables. HiTailor constructs an abstract model, which defines row/column headings as biclustering and hierarchical structures. Based on our abstract model, we identify three pairs of operators, Swap/Transpose, ToStacked/ToLinear, Fold/Unfold, for transformations of hierarchical tables to support users’ comprehensive explorations. After transformation, users can specify a cell or block of interest in hierarchical tables as a TableUnit for visualization, and HiTailor recommends other related TableUnits according to the abstract model using different mechanisms. We demonstrate the usability of the HiTailor system through a comparative study and a case study with domain experts, showing that HiTailor can present and explore hierarchical tables from different viewpoints. HiTailor is available at https://github.com/bitvis2021/HiTailor.

Declarative grammar has becoming an increasingly significant technique for understanding visualization design space. We propose GoTreeScape system to facilitate the navigation and exploration of the vast design space implied by GoTree, a declarative grammar of tree visualizations. To provide users with the design space overview, GoTreeScape utilizes an Encoder-Decoder architecture to project tree visualizations into a 2D landscape, taking into account the relationships between different design features. Furthermore, GoTreeScape includes an exploratory framework, enabling top-down, bottom-up, and hybrid modes to support the inherently undirected nature of exploratory search. We demonstrate that GoTreeScape can expand the diversity of user-designed tree visualizations through two case studies.

We propose BarcodeTree (BCT), a novel visualization technique for comparing topological structures and node attribute values of multiple trees. BCT can provide an overview of one hundred shallow and stable trees simultaneously, without aggregating individual nodes. Each BCT is shown within a single row using a style similar to a barcode, allowing trees to be stacked vertically with matching nodes aligned horizontally to ease comparison and maintain space efficiency. We design several visual cues and interactive techniques to help users understand the topological structure and compare trees. In an experiment comparing two variants of BCT with icicle plots, the results suggest that BCTs make it easier to visually compare trees by reducing the vertical distance between different trees. We also present two case studies involving a dataset of hundreds of trees to demonstrate BCT’s utility.

We present GoTree, a declarative grammar allowing users to instantiate tree visualizations by specifying three aspects: vi- sual elements, layout, and coordinate system. Within the set of all possible tree visualization techniques, we identify a subset of techniques that are both “unit-decomposable” and “axis-decomposable” (terms we define). For tree visualiza- tions within this subset, GoTree gives the user flexible and fine-grained control over the parameters of the techniques, supporting both explicit and implicit tree visualizations. We developed Tree Illustrator, an interactive authoring tool based on GoTree grammar. Tree Illustrator allows users to create a considerable number of tree visualizations, including not only existing techniques but also undiscovered and hybrid visualizations. We demonstrate the expressiveness and generative power of GoTree with a gallery of examples and conduct a qualitative study to validate the usability of Tree Illustrator.